The use of IoT data integration is mainly due to the enormous amount of data collected, the variety of data collected from different sources, and the accuracy of the data collected.
Fremont, CA: The Internet of Things (IoT) is emerging rapidly as a technological necessity for modern enterprises. And, realizing this, businesses are stepping up their efforts to implement and expand their IoT networks. However, while businesses are increasingly investing in IoT initiatives, they are facing a number of challenges associated with the adoption of technology. The need for high-capacity communication networks, the security implications of using a myriad of smart connected devices, and the issue of data integration in IoT are among the most significant challenges for IoT adoption.
Comprehending the Need for Data Integration in IoT
Since its inception, its ability to provide remote control and visibility over enterprise-wide processes has been one of the primary attractive factors for IoT. IoT is also seen as instrumental in the end-to-end integration of various business units and processes, which can result in better coordination between these entities, leading to enhanced business performance. However, business and technology leaders are increasingly emphasizing that the real value of IoT lies in the data it generates. And in order to leverage data for any practical purpose, it is important to collect the data generated by the different sources.This need for data collection or data integration in IoT, as it is known, can be more challenging than for earlier forms of analytics such as big data.
Exploring the challenge of IoT data integration
The use of IoT data integration is mainly due to the enormous amount of data collected, the variety of data collected from different sources, and the accuracy of the data collected. However, the same elements are what stand as barriers to IoT data integration.The ever-increasing number of connected devices that constantly collect data from the edges of the enterprise network makes it more difficult for the enterprise to keep track of all data flowing from different directions.
In addition, all the data collected from the endpoints also come with a lot of noise, repetitive information, and other issues that make data difficult to use. Thus, before compiling all the data in a single repository, such as a data warehouse, it is vital to clean up the data and make it usable.This adds to the need for investment in specialised tools, processes and personnel for the cleaning and structuring of data.